{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/sebastiantonke/Desktop/Replication Package/Replication files experiment/output_logs/table A17.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}12 Jul 2023, 03:57:57
{txt}
{com}. 
. 
. 
. set seed 3125
{txt}
{com}. 
. 
. * Install user-written cem command
. * ssc install cem
. preserve
{txt}
{com}. gen treatmenttomatch=0 if itt==.
{txt}(207,212 missing values generated)

{com}. replace treatmenttomatch=1 if itt==0
{txt}(69,124 real changes made)

{com}. drop if treatmenttomatch==. 
{txt}(138,088 observations deleted)

{com}. drop if t>12
{txt}(131,414 observations deleted)

{com}. collapse (mean) meanasinhinvoice inactive prenoconsumption paidcount1 paidcount2 meanaveragepayment1 meanaveragepayment2 preclosingbalancew  presumpaymentratiow  preage_of_accountw if t<209, by(customer treatmenttomatch)
{txt}
{com}. cem  meanasinhinvoice (#5) inactive (#5) prenoconsumption(#5) paidcount1 (#5) paidcount2 (#5) meanaveragepayment1(#5) meanaveragepayment2(#5) preclosingbalancew(#5)  presumpaymentratiow(#5) preage_of_accountw(#5), treatment(treatmenttomatch) k2k 
{res}
{txt}Matching Summary:
-----------------
Number of strata: {res}2576
{txt}Number of matched strata: {res}628

           {txt}   0     1
      All  {res}9439  3300
{txt}  Matched  {res}2501  2501
{txt}Unmatched  {res}6938   799


{txt}Multivariate L1 distance: {res}.96561375

{txt}Univariate imbalance:

                          L1     mean      min      25%      50%      75%      max
   meanasinhinvoice  {res} .05358  -.01095  -.00864   .01777  -.01927  -.03054   1.1679
{txt}           inactive  {res}      0        0        0        0        0        0        0
{txt}   prenoconsumption  {res} .02399  -.00088        0        0        0  -.03175        0
{txt}         paidcount1  {res} .01479   .00147        0        0        0        0        .
{txt}         paidcount2  {res}   .012   .00174        0        0        0        0        0
{txt}meanaveragepayment1  {res} .03079   .01708        0   .11567   .00823  -.00206   .20143
{txt}meanaveragepayment2  {res} .02039   .00434        0   .00175   .00161   .01811  -.49041
{txt} preclosingbalancew  {res} .04358  -3.1468        0      .52     2.71   -19.98        0
{txt}presumpaymentratiow  {res} .05278  -.00426        0    .0221  -.00388  -.00168        0
{txt} preage_of_accountw  {res} .09436  -2.8489        0       -3       -2        0        0
{txt}
{com}. bysort treatmenttomatch: tabstat meanasinhinvoice inactive prenoconsumption paidcount1 paidcount2 meanaveragepayment1 meanaveragepayment2 preclosingbalancew  presumpaymentratiow preage_of_accountw [aweight=cem_weights] if cem_matched==1, by(treatmenttomatch) s(me v) nototal

{txt}{hline}
-> treatmenttomatch = 0

Summary statistics: mean, variance
  by categories of: treatmenttomatch 

{ralign 16:treatmenttomatch} {...}
{c |}{...}
  meanas~e  inactive  prenoc~n  paidco~1  paidco~2  meanav~1  meanav~2  preclo~w  presum~w  preage~w
{hline 17}{c +}{hline 100}
{ralign 16:0} {...}
{c |}{...}
 {res} 4.461908  .0991603  .1012344  .3258828  .3506064  3.996288  4.439059  432.9056  .8286709  46.20952
{txt}{space 16} {...}
{c |}{...}
 {res} 1.380987  .0893633  .0304027  .0619989  .0652453  5.539174  4.493613  973383.4   .205203  1650.468
{txt}{hline 17}{c BT}{hline 100}

{hline}
-> treatmenttomatch = 1

Summary statistics: mean, variance
  by categories of: treatmenttomatch 

{ralign 16:treatmenttomatch} {...}
{c |}{...}
  meanas~e  inactive  prenoc~n  paidco~1  paidco~2  meanav~1  meanav~2  preclo~w  presum~w  preage~w
{hline 17}{c +}{hline 100}
{ralign 16:1} {...}
{c |}{...}
 {res} 4.450953  .0991603  .1003588  .3274364  .3523457  4.013366  4.443396  429.7587  .8244086  43.36066
{txt}{space 16} {...}
{c |}{...}
 {res} 1.335614  .0893633  .0296299  .0623145  .0660123  5.595526  4.482253  987463.2  .1925497  1587.313
{txt}{hline 17}{c BT}{hline 100}

{com}. 
. keep if cem_matched==1
{txt}(7,737 observations deleted)

{com}. gen matchedcontrol=1
{txt}
{com}. 
. keep customer matchedcontrol cem_weights
{txt}
{com}. save "$filepath/temp/matchedcontrol", replace // generates a new_datafile with the matching weights
{txt}file /Users/sebastiantonke/Desktop/Replication Package/Replication files experiment/temp/matchedcontrol.dta saved

{com}. restore
{txt}
{com}. 
. ** merge in matched control group *
. merge m:1 customer using "$filepath/temp/matchedcontrol", keepusing(matchedcontrol cem_weights) nogen update
{res}
{txt}{col 5}Result{col 38}# of obs.
{col 5}{hline 41}
{col 5}not matched{col 30}{res}         297,699
{txt}{col 9}from master{col 30}{res}         297,699{txt}  
{col 9}from using{col 30}{res}               0{txt}  

{col 5}matched{col 30}{res}         105,844
{txt}{col 9}not updated{col 30}{res}         105,844{txt}  
{col 9}missing updated{col 30}{res}               0{txt}  
{col 9}nonmissing conflict{col 30}{res}               0{txt}  
{col 5}{hline 41}

{com}. bysort customer: egen matchedcontrol2=mean(matchedcontrol)
{txt}(297699 missing values generated)

{com}. replace matchedcontrol=matchedcontrol2
{txt}(0 real changes made)

{com}. drop matchedcontrol2
{txt}
{com}. 
. gen cem_match=0 if matchedcontrol==1 & itt==.
{txt}(350,651 missing values generated)

{com}. replace cem_match=1 if itt==0 & matchedcontrol==1
{txt}(52,952 real changes made)

{com}. 
. 
. *********************************
. **** Initial month (October) ****
. *********************************
. 
. probit paidcount cem_match  if t==14 ,vce(cluster customer)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -3435.628}  
Iteration 1:{space 3}log pseudolikelihood = {res:-3410.4697}  
Iteration 2:{space 3}log pseudolikelihood = {res:-3410.4694}  
Iteration 3:{space 3}log pseudolikelihood = {res:-3410.4694}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}     4,976
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     50.20
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-3410.4694{txt}{col 49}Pseudo R2{col 67}= {res}    0.0073

{txt}{ralign 78:(Std. Err. adjusted for {res:4,976} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   paidcount{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .2529227{col 26}{space 2}  .035699{col 37}{space 1}    7.08{col 46}{space 3}0.000{col 54}{space 4}  .182954{col 67}{space 3} .3228914
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.2197229{col 26}{space 2} .0253713{col 37}{space 1}   -8.66{col 46}{space 3}0.000{col 54}{space 4}-.2694497{col 67}{space 3}-.1699962
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,976
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(paidcount), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:cem_match}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .0996724{col 26}{space 2} .0138449{col 37}{space 1}    7.20{col 46}{space 3}0.000{col 54}{space 4}  .072537{col 67}{space 3} .1268079
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg lnpayment cem_match if t==14, vce(cluster customer)

{txt}Linear regression                               Number of obs     = {res}     2,305
                                                {txt}F(1, 2304)        =  {res}     0.50
                                                {txt}Prob > F          = {res}    0.4808
                                                {txt}R-squared         = {res}    0.0002
                                                {txt}Root MSE          =    {res} 1.0477

{txt}{ralign 78:(Std. Err. adjusted for {res:2,305} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   lnpayment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .0309894{col 26}{space 2} .0439477{col 37}{space 1}    0.71{col 46}{space 3}0.481{col 54}{space 4}-.0551917{col 67}{space 3} .1171706
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.134333{col 26}{space 2} .0328449{col 37}{space 1}  156.32{col 46}{space 3}0.000{col 54}{space 4} 5.069924{col 67}{space 3} 5.198741
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. preserve 
{txt}
{com}. keep if t==14
{txt}(384,211 observations deleted)

{com}. capture program drop Ey_boot
{txt}
{com}. program define Ey_boot, eclass
{txt}  1{com}. twopm payment cem_match, firstpart(probit) secondpart(regress, log) vce(cluster customer)
{txt}  2{com}. margins, dydx(cem_match) predict(duan) nose post
{txt}  3{com}. end
{txt}
{com}. bootstrap _b, seed(3125) reps(1000): Ey_boot
{txt}(running Ey_boot on estimation sample)

Bootstrap replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}     4,976
{txt}{col 49}Replications{col 67}= {res}     1,000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} 34.63872{col 26}{space 2} 7.777542{col 37}{space 1}    4.45{col 46}{space 3}0.000{col 54}{space 4} 19.39502{col 67}{space 3} 49.88242
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. restore
{txt}
{com}. 
. reg asinhpayment cem_match if t==14, vce(cluster customer)

{txt}Linear regression                               Number of obs     = {res}     4,976
                                                {txt}F(1, 4975)        =  {res}    50.26
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0100
                                                {txt}Root MSE          =    {res} 2.9858

{txt}{ralign 78:(Std. Err. adjusted for {res:4,976} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}asinhpayment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .6001099{col 26}{space 2} .0846499{col 37}{space 1}    7.09{col 46}{space 3}0.000{col 54}{space 4} .4341588{col 67}{space 3} .7660611
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.407057{col 26}{space 2} .0591587{col 37}{space 1}   40.69{col 46}{space 3}0.000{col 54}{space 4}  2.29108{col 67}{space 3} 2.523035
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. *************************************
. **** Medium term (November-June) ****
. *************************************
. 
. probit paidcount cem_match if t>=15 & t<=22 ,vce(cluster customer)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-25903.739}  
Iteration 1:{space 3}log pseudolikelihood = {res:-25849.478}  
Iteration 2:{space 3}log pseudolikelihood = {res:-25849.476}  
{res}
{txt}Probit regression{col 49}Number of obs{col 67}= {res}    39,549
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     49.99
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-25849.476{txt}{col 49}Pseudo R2{col 67}= {res}    0.0021

{txt}{ralign 78:(Std. Err. adjusted for {res:4,969} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   paidcount{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .1343826{col 26}{space 2}  .019007{col 37}{space 1}    7.07{col 46}{space 3}0.000{col 54}{space 4} .0971295{col 67}{space 3} .1716357
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.4193183{col 26}{space 2} .0134955{col 37}{space 1}  -31.07{col 46}{space 3}0.000{col 54}{space 4} -.445769{col 67}{space 3}-.3928675
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. margins, dydx(*) post
{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    39,549
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Pr(paidcount), predict()}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col:dy/dx w.r.t.}:{space 1}{res:cem_match}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}      dy/dx{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .0502919{col 26}{space 2} .0070862{col 37}{space 1}    7.10{col 46}{space 3}0.000{col 54}{space 4} .0364032{col 67}{space 3} .0641806
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. reg lnpayment cem_match if t>=15 & t<=22 , vce(cluster customer)

{txt}Linear regression                               Number of obs     = {res}    14,346
                                                {txt}F(1, 4370)        =  {res}    10.79
                                                {txt}Prob > F          = {res}    0.0010
                                                {txt}R-squared         = {res}    0.0020
                                                {txt}Root MSE          =    {res} 1.0172

{txt}{ralign 78:(Std. Err. adjusted for {res:4,371} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}   lnpayment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2}-.0919511{col 26}{space 2} .0279931{col 37}{space 1}   -3.28{col 46}{space 3}0.001{col 54}{space 4}-.1468318{col 67}{space 3}-.0370704
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 5.238905{col 26}{space 2} .0202471{col 37}{space 1}  258.75{col 46}{space 3}0.000{col 54}{space 4} 5.199211{col 67}{space 3}   5.2786
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. preserve 
{txt}
{com}. keep if t>=15 & t<=22 
{txt}(245,229 observations deleted)

{com}. capture program drop Ey_boot
{txt}
{com}. program define Ey_boot, eclass
{txt}  1{com}. twopm payment cem_match i.t, firstpart(probit) secondpart(regress, log) vce(cluster customer)
{txt}  2{com}. margins, dydx(cem_match) predict(duan) nose post
{txt}  3{com}. end
{txt}
{com}. bootstrap _b, seed(3125) reps(1000): Ey_boot
{txt}(running Ey_boot on estimation sample)

Bootstrap replications ({res}1000{txt})
{hline 4}{c +}{hline 3} 1 {hline 3}{c +}{hline 3} 2 {hline 3}{c +}{hline 3} 3 {hline 3}{c +}{hline 3} 4 {hline 3}{c +}{hline 3} 5 
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{res}
{txt}Average marginal effects{col 49}Number of obs{col 67}= {res}    39,549
{txt}{col 49}Replications{col 67}= {res}     1,000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}   Observed{col 26}   Bootstrap{col 54}         Norm{col 67}al-based
{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} 4.874909{col 26}{space 2} 2.429639{col 37}{space 1}    2.01{col 46}{space 3}0.045{col 54}{space 4}  .112903{col 67}{space 3} 9.636914
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. restore
{txt}
{com}. 
. reg asinhpayment cem_match  if t>=15 & t<=22, vce(cluster customer)

{txt}Linear regression                               Number of obs     = {res}    39,549
                                                {txt}F(1, 4968)        =  {res}    42.14
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0021
                                                {txt}Root MSE          =    {res} 2.8912

{txt}{ralign 78:(Std. Err. adjusted for {res:4,969} clusters in customer)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}asinhpayment{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}cem_match {c |}{col 14}{res}{space 2} .2630529{col 26}{space 2} .0405231{col 37}{space 1}    6.49{col 46}{space 3}0.000{col 54}{space 4} .1836096{col 67}{space 3} .3424961
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.002037{col 26}{space 2}  .028479{col 37}{space 1}   70.30{col 46}{space 3}0.000{col 54}{space 4} 1.946206{col 67}{space 3} 2.057869
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. estpost tabstat paidcount lnpayment payment asinhpayment if t==14 & cem_match==0, by(cem_match) statistics(mean sd n) columns(statistics) nototal

{txt}Summary statistics: mean sd count
     for variables: paidcount lnpayment payment asinhpayment
  by categories of: cem_match

{space 0}{space 0}{ralign 12:cem_match}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:0}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:paidcount}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .4130435}}}{space 1}{space 1}{ralign 9:{res:{sf: .4924796}}}{space 1}{space 1}{ralign 9:{res:{sf:     2484}}}{space 1}
{space 0}{space 0}{ralign 12:lnpayment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 5.134333}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.052118}}}{space 1}{space 1}{ralign 9:{res:{sf:     1026}}}{space 1}
{space 0}{space 0}{ralign 12:payment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 125.0134}}}{space 1}{space 1}{ralign 9:{res:{sf:  332.724}}}{space 1}{space 1}{ralign 9:{res:{sf:     2484}}}{space 1}
{space 0}{space 0}{ralign 12:asinhpayment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 2.407057}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.948456}}}{space 1}{space 1}{ralign 9:{res:{sf:     2484}}}{space 1}

{com}. 
. estpost tabstat  paidcount lnpayment payment asinhpayment if t>=15 & t<=22 & cem_match==0, by(cem_match) statistics(mean sd n) columns(statistics) nototal

{txt}Summary statistics: mean sd count
     for variables: paidcount lnpayment payment asinhpayment
  by categories of: cem_match

{space 0}{space 0}{ralign 12:cem_match}{space 1}{c |}{space 1}{ralign 9:e(mean)}{space 1}{space 1}{ralign 9:e(sd)}{space 1}{space 1}{ralign 9:e(count)}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:0}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:paidcount}{space 1}{c |}{space 1}{ralign 9:{res:{sf: .3374918}}}{space 1}{space 1}{ralign 9:{res:{sf: .4728662}}}{space 1}{space 1}{ralign 9:{res:{sf:    19719}}}{space 1}
{space 0}{space 0}{ralign 12:lnpayment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 5.238905}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.011168}}}{space 1}{space 1}{ralign 9:{res:{sf:     6655}}}{space 1}
{space 0}{space 0}{ralign 12:payment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 107.1113}}}{space 1}{space 1}{ralign 9:{res:{sf: 306.8694}}}{space 1}{space 1}{ralign 9:{res:{sf:    19719}}}{space 1}
{space 0}{space 0}{ralign 12:asinhpayment}{space 1}{c |}{space 1}{ralign 9:{res:{sf: 2.002037}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.865922}}}{space 1}{space 1}{ralign 9:{res:{sf:    19719}}}{space 1}

{com}. 
. log close
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 {txt}closed on:  {res}12 Jul 2023, 04:15:18
{txt}{.-}
{smcl}
{txt}{sf}{ul off}